{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#解决中文显示问题\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
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       "      <td>2785</td>\n",
       "      <td>62</td>\n",
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       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
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       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
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       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
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       "      <td>76.000000</td>\n",
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       "      <td>17</td>\n",
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       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
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       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
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       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
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       "      <th>476</th>\n",
       "      <td>17</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0        0  2785      62  170  72.599998  25.120001  \n",
       "1       60  3133      68  174  52.700001  17.410000  \n",
       "2        0  3901      80  169  46.500000  16.280001  \n",
       "3        0  4946     100  183  79.699997  23.799999  \n",
       "4       68  3538      74  171  54.700001  18.709999  \n",
       "..     ...   ...     ...  ...        ...        ...  \n",
       "472      0  4647     100  176  69.500000  22.440001  \n",
       "473     50  7042     100  177  76.000000  24.260000  \n",
       "474     85  5755     100  181  65.000000  19.840000  \n",
       "475     76  5688     100  172  51.700001  17.480000  \n",
       "476      0     0       0    0   0.000000   0.000000  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male = pd.read_excel('.\\体测分数_男生.xls')\n",
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\86131\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  app.launch_new_instance()\n",
      "D:\\Users\\86131\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b7ea6183c8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male1 = male.loc[:,'男1000米跑分数']\n",
    "male1 = pd.cut(male1,bins = 3)\n",
    "data = male1.value_counts()\n",
    "tmp = pd.DataFrame(data)\n",
    "male2 = male.loc[:,'男引体分数']\n",
    "male2 = pd.cut(male2,bins = 3)\n",
    "data2 = male2.value_counts()\n",
    "tmp2 = pd.DataFrame(data2)\n",
    "\n",
    "plt.figure(figsize = (12,12))\n",
    "plt.subplot(121)\n",
    "patches,l_text,p_text=plt.pie(x=tmp,\n",
    "                            labels =[\"high\", \"mid\", \"low\"],\n",
    "                            autopct = '%0.1f%%',\n",
    "                            pctdistance = 0.5,\n",
    "                            shadow = True\n",
    "                           )\n",
    "for t in p_text:\n",
    "    t.set_size(15)\n",
    "for t in l_text:\n",
    "    t.set_size(18)\n",
    "plt.title(label = '男生1000米跑成绩',fontsize = 18)\n",
    "plt.legend(['high','mid','low'])\n",
    "    \n",
    "plt.subplot(122)\n",
    "patches,l_text,p_text=plt.pie(x=tmp2,\n",
    "                            labels =[\"high\", \"mid\", \"low\"],\n",
    "                            autopct = '%0.1f%%',\n",
    "                            pctdistance = 0.5,\n",
    "                            shadow = True\n",
    "                           )\n",
    "for t in p_text:\n",
    "    t.set_size(15)\n",
    "for t in l_text:\n",
    "    t.set_size(18)\n",
    "plt.title(label = '男生引体向上成绩',fontsize = 18)\n",
    "plt.legend(['high','mid','low'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0     85  3775     100  163.0  51.299999  19.309999  \n",
       "1     66  3683     100  163.0  66.599998  25.070000  \n",
       "2     76  3331     100  157.0  60.000000  24.340000  \n",
       "3     85  3701     100  160.0  50.700001  19.799999  \n",
       "4     70  3592     100  167.0  63.900002  22.910000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female= pd.read_excel('.\\体测分数_女生.xls')\n",
    "female.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#取出女生800米跑成绩，并等宽分4组\n",
    "female1 = female.loc[:,'女800米跑分数']\n",
    "female2 = pd.cut(female1,bins = 4,right =True)\n",
    "#统计各分组人数\n",
    "data = female2.value_counts()\n",
    "tmp =list(data)\n",
    "# print(tmp)\n",
    "#取出女生跳远成绩，并等宽分4组\n",
    "female3 = female.loc[:,'女跳远分数']\n",
    "female4 = pd.cut(female3,bins = 4,right =True)\n",
    "#统计各分组人数\n",
    "data2 =pd.value_counts(female4)\n",
    "tmp2 =list(data2[::-1])\n",
    "#画图\n",
    "#女800米跑成绩（用hist画图）\n",
    "plt.figure(figsize = (15,5))\n",
    "plt.subplot(1,2,1)\n",
    "_ = plt.hist(female1,bins = 4,label = tmp)\n",
    "plt.title ('女生800米跑成绩',fontsize = 18)\n",
    "plt.xticks([0,25,50,75,100],fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.text(8,60,tmp[-1],size = 20)\n",
    "plt.text(35,50,tmp[-2],size = 20)\n",
    "plt.text(56,200,tmp[-4],size = 20)\n",
    "plt.text(85,400,tmp[-3],size = 20)\n",
    "ax= plt.gca()\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "#女生跳远成绩\n",
    "plt.subplot(1,2,2)\n",
    "_ = plt.hist(female3,bins = 4,label = tmp2)\n",
    "plt.title ('女生跳远成绩',fontsize = 18)\n",
    "plt.xticks([0,25,50,75,100],fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "plt.text(8,60,tmp2[1],size = 20)\n",
    "plt.text(35,50,tmp2[0],size = 20)\n",
    "plt.text(56,420,tmp2[-1],size = 20)\n",
    "plt.text(85,160,tmp2[-2],size = 20)\n",
    "ax= plt.gca()\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      25.120001\n",
       "1      17.410000\n",
       "2      16.280001\n",
       "3      23.799999\n",
       "4      18.709999\n",
       "         ...    \n",
       "472    22.440001\n",
       "473    24.260000\n",
       "474    19.840000\n",
       "475    17.480000\n",
       "476     0.000000\n",
       "Name: BMI, Length: 477, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmi_1 = male.loc[:,'BMI']\n",
    "bmi_2 = female.loc[:,'BMI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\86131\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#男生BMI分组\n",
    "bins = [min(bmi_1)-1,16.4,23.2,26.3,max(bmi_1)+1]\n",
    "labels = ['低体重','正常','超重','肥胖']\n",
    "aa= pd.cut(bmi_1, bins, right = False, labels= labels).value_counts(ascending = False)\n",
    "#获取各组名称作为饼图标签\n",
    "indexs = aa._stat_axis.values.tolist()\n",
    "plt.figure(figsize = (5,8))\n",
    "#画大饼图\n",
    "patches,l_text,p_text = plt.pie(aa,\n",
    "                                radius = 2,\n",
    "                                colors = ['tomato', 'lightskyblue', 'goldenrod', 'green'],\n",
    "                                autopct = '%0.1f%%',\n",
    "                                pctdistance = 0.85,\n",
    "                                wedgeprops=dict(linewidth=3,width=0.8,edgecolor='w'))\n",
    "plt.title('男女生体重指数对比',pad =110,fontsize = 22)\n",
    "#设置比例标签字号\n",
    "for t in p_text:\n",
    "    t.set_size(15)\n",
    "#添加男生图例，并保存为\"l1\"\n",
    "l1 = plt.legend(indexs,labels = indexs,bbox_to_anchor = (1.1,0.3,0.4,1),title ='男生指数')\n",
    "#女生BMI分组\n",
    "bins2 = [min(bmi_2)-1,16.4,22.7,25.2,max(bmi_2)+1]\n",
    "aa2= pd.cut(bmi_2, bins2, right = False, labels= labels).value_counts(ascending = False)\n",
    "#获取各组名称作为饼图标签\n",
    "indexs2 = aa2._stat_axis.values.tolist()\n",
    "#画小饼图\n",
    "patches2,l_text2,p_text2 = plt.pie(aa2,\n",
    "                                radius =1.2,\n",
    "                                autopct = '%0.1f%%',\n",
    "                                colors = ['gold', 'salmon', 'violet', 'teal'],\n",
    "                                pctdistance = 0.7,\n",
    "                                wedgeprops=dict(linewidth=3,width=0.7,edgecolor='w'))\n",
    "l2 = plt.legend(patches2,indexs2,bbox_to_anchor = (1.38,0.3,0.4,1),title ='女生指数')\n",
    "#重新显示男生图例\n",
    "plt.gca().add_artist(l1)\n",
    "#设置比例标签字号\n",
    "for t in p_text2:\n",
    "    t.set_size(15)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
